ORCID Profile
0000-0002-7887-2613
Current Organisation
Nanyang Technological University
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Informa UK Limited
Date: 15-06-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: MDPI AG
Date: 03-01-2020
DOI: 10.3390/EN13010243
Abstract: This paper presents the methodology to detect and identify the type of fault that occurs in the shunt compensated static synchronous compensator (STATCOM) transmission line using a combination of Discrete Wavelet Transform (DWT) and Naive Bayes (NB) classifiers. To study this, the network model is designed using Matlab/Simulink. Different types of faults, such as Line to Ground (LG), Line to Line (LL), Double Line to Ground (LLG) and the three-phase (LLLG) fault, are applied at disparate zones of the system, with and without STATCOM, considering the effect of varying fault resistance. The three-phase fault current waveforms obtained are decomposed into several levels using Daubechies (db) mother wavelet of db4 to extract the features, such as the standard deviation (SD) and energy values. Then, the extracted features are used to train the classifiers, such as Multi-Layer Perceptron Neural Network (MLP), Bayes and the Naive Bayes (NB) classifier to classify the type of fault that occurs in the system. The results obtained reveal that the proposed NB classifier outperforms in terms of accuracy rate, misclassification rate, kappa statistics, mean absolute error (MAE), root mean square error (RMSE), percentage relative absolute error (% RAE) and percentage root relative square error (% RRSE) than both MLP and the Bayes classifier.
Publisher: Elsevier BV
Date: 12-2019
Publisher: MDPI AG
Date: 13-10-2019
DOI: 10.20944/PREPRINTS201910.0148.V1
Abstract: This paper presents the methodology to detect and identify the type of fault that occurs in shunt connected static synchronous compensator (STATCOM) transmission line using a combination of Discrete Wavelet Transform (DWT) and Naive Bayes classifier. To study this, the network model is designed using Mat-lab/Simulink. The different faults such as Line to Ground (LG), Line to Line (LL), Double Line to Ground (LLG) and three-phase (LLLG) fault are applied at different zones of system with and without STATCOM considering the effect of varying fault resistance. The three-phase fault current waveforms obtained are decomposed into several levels using daubechies mother wavelet of db4 to extract the features such as standard deviation and Energy values. The extracted features are used to train the classifiers such as Multi-Layer Perceptron Neural Network (MLP), Bayes and Naive Bayes (NB) classifier to classify the type of fault that occurs in the system. The results reveal that the proposed NB classifier outperforms in terms of accuracy rate, misclassification rate, kappa statistics, mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE) and root-relative square error (RRSE) than MLP and Bayes classifier.
Publisher: MDPI AG
Date: 04-12-2019
DOI: 10.3390/SU11246908
Abstract: This paper proposes a new population-based hybrid particle swarm optimized-gravitational search algorithm (PSO-GSA) for tuning the parameters of the proportional-integral-derivative (PID) controller of a two-area interconnected dynamic power system with the presence of nonlinearities such as generator rate constraints (GRC) and governor dead-band (GDB). The tuning of controller parameters such as Kp, Ki, and Kd are obtained by minimizing the objective function formulated using the steady-state performance indices like Integral absolute error (IAE) of tie-line power and frequency deviation of interconnected system. To test the robustness of the propounded controller, the system is studied with system uncertainties, such as change in load demand, synchronizing power coefficient and inertia constant. The two-area interconnected power system (TAIPS) is modeled and simulated using Matlab/Simulink. The results exhibit that the steady-state and transient performance indices such as IAE, settling time, and control effort are impressively enhanced by an amount of 87.65%, 15.39%, and 91.17% in area-1 and 86.46%, 41.35%, and 91.04% in area-2, respectively, by the proposed method compared to other techniques presented. The minimum control effort of PSO-GSA-tuned PID controller depicts the robust performance of the controller compared to other non-meta-heuristic and meta-heuristic methods presented. The proffered method is also validated using the hardware-in-the-loop (HIL) real-time digital simulation to study the effectiveness of the controller.
Publisher: Elsevier BV
Date: 09-2022
Publisher: MDPI AG
Date: 07-05-2022
DOI: 10.3390/SU14095668
Abstract: In this work, a chaotic search-based hybrid Sperm Swarm Optimized-Gravitational Search Algorithm (CSSO-GSA) is proposed for automatic load frequency control (ALFC) of a hybrid power system (HPS). The HPS model is developed using multiple power sources (thermal, bio-fuel, and renewable energy (RE)) that generate power to balance the system’s demand. To regulate the frequency of the system, the control parameters of the proportional-integral-derivative (PID) controller for ALFC are obtained by minimizing the integral time absolute error of HPS. The effectiveness of the proposed technique is verified with various combinations of power sources (all sources, thermal with bio-fuel, and thermal with RE) connected into the system. Further, the robustness of the proposed technique is investigated by performing a sensitivity analysis considering load variation and weather intermittency of RE sources in real-time. However, the type of RE source does not have any severe impact on the controller but the uncertainties present in RE power generation required a robust controller. In addition, the effectiveness of the proposed technique is validated with comparative and stability analysis. The results show that the proposed CSSO-GSA strategy outperforms the SSO, GSA, and hybrid SSO-GSA methods in terms of steady-state and transient performance indices. According to the results of frequency control optimization, the main performance indices such as settling time (ST) and integral time absolute error (ITAE) are significantly improved by 60.204% and 40.055% in area 1 and 57.856% and 39.820% in area 2, respectively, with the proposed CSSO-GSA control strategy compared to other existing control methods.
No related grants have been discovered for Veerapandiyan Veerasamy.